
\subsection{Image Quality}
The visual quality of the STABYLO scheme is evaluated in this section.
Three metrics are computed in these experiments : 
the Peak Signal to Noise Ratio (PSNR), 
the PSNR-HVS-M~\cite{PSECAL07,psnrhvsm11} and the BIQI~\cite{MB10,biqi11}.
The first one is widely used but does not take into
account Human Visual System (HVS).
The two last ones have been designed to tackle this problem.



biqi = 28.3
psnr-hvs-m= 78,6
psnr-hvs= 67.3


\subsection{Steganalysis}



The quality of our approach has been evaluated through the two 
AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
Both aims at detecting hidden bits in grayscale natural images and are 
considered as the state of the art of steganalysers in spatial domain~\cite{FK12}.
The former approach is based on a simplified parametric model of natural images.
Parameters are firstly estimated and a adaptive Asymptotically Uniformly Most Powerful
(AUMP) test is designed (theoretically and practically) to check whether
a natural image has stego content or not.  
In the latter, the authors show that the 
machine learning step, (which is often
implemented as support vector machine)
can be a favourably executed thanks to an Ensemble Classiﬁers.



\JFC{Raphael, il faut donner des résultats ici}